bayesprot: Fit the BayesProt Bayesian Protein-level quantification model
In biospi/bayesprot: Bayesian Protein-level Quantification for Proteomics

Fit the BayesProt Bayesian Protein-level quantification model

bayesprot(data, data.design = new_design(data), ref.assays = "ref",
  norm.func = list(median = norm_median), dea.func = NULL,
  fdr.func = list(ash = fdr_ash), feature.vars = FALSE,
  peptide.vars = FALSE, peptide.deviations = FALSE, plots = FALSE,
  output = "bayesprot", control = new_control())

`data`	A data.frame of input data as returned by import_ProteinPilot or import_ProteomeDiscoverer.
`data.design`	Optionally, a data.frame created by design and then customised, which specifies assay-level study design, including reference assays, assay info and any covariates for optional differential expression analysis. By default, all assays are set as reference channels, which is appropriate only for label-free studies and fully-blocked iTraq/TMT/SILAC designs.
`norm.func`	A normalisation function or list of functions to run, or NULL (default: median normalisation)
`dea.func`	A differential expression analysis function or list of functions to run, or NULL (default: NULL)
`fdr.func`	A false discovery rate (FDR) correction function or list of functions to run, or NULL (default: ash FDR correction)
`output`	Folder on disk whether all intermediate and output data will be stored; default is `"bayesprot"`.
`control`	A control object created with new_control specifying control parameters for the model.
`plot`	Generate all plots

A bayesprot_fit object that can be interrogated for various results with protein_quants, peptide_deviations, peptide_stdevs, feature_stdevs, de_metafor and de_mice. del deletes all associated files on disk.